1. Field of the Invention
The present invention is directed to a method, system, and a non-transitory, computer-readable data storage medium for diagnosis detection in a system. More precisely, the method, system and storage medium of the present invention are based on an interactive diagnosis detection technique.
2. Description of the Prior Art
For industry, there is a continued need for improved prognostics and diagnostic management of systems.
Conventionally, the concept of fault diagnostics has been understood as the detection of an existing problem or failure in a system, in order to correct it. Today's advances are raising the bar toward machine prognostics, where failure modes and the remaining life of a system can be predicted, and quick and exact intervention may be performed to repair the identified faults. Novel solutions are responsive at least to the need for efficient operations and reduced maintenance costs.
However, particularly for large-scale systems, diagnostic and prognosis are employed for predicting and preventing failures so that expensive equipment does not destroy itself or need to be discarded. Having more data on the faults found (diagnostics) and life expectancy (prognostics) can only help.
Therefore, a further need is to reduce the current reliance on diagnostic sensors, which already exist in many industrial systems, in favor of intelligent systems that correlate the data from these sensors and use it to make predictions and render possible diagnostics.
Improvements in prognosis and diagnostic through the development of better sensors and more effective algorithms have provided more lead time to respond to evolving failures. In addition, improved prognosis and diagnostic brings the benefits of increased safety and operational efficiency, reduction in lost operational hours, lower maintenance costs, and decreased likelihood of secondary damage from failing system components, reduced inventory requirements, extended subcomponent life, and improved product quality. A more fully developed prognosis and diagnostic capability enables the better gauge when an impending system failure is acceptable, and when it isn't, especially in industrial systems where some failures may take lives and cost millions. In addition, better developed prognosis and diagnostic systems will help judge when redundancy is needed in a system, by determining which mission-critical systems need backup. Prognosis and diagnostic systems also help reliably decide when to switch to a redundant system, if one exists, by providing more accurate mechanisms for detecting and predicting impending failures.
Therefore a need still exists for an intelligent automated prognosis and diagnostic methods that may be applicable to a wide range of industrial systems.